Neural Network Prediction of Mass Transfer Coefficients in Distillation Columns Using Aspen Hysys
DOI:
https://doi.org/10.31272/jeasd.2686Keywords:
Aspen Hysys, Distillation, Mass Transfer Coefficient, Neural Network, SimulationAbstract
Predicting the mass transfer coefficient (K) is one of the most important parameters in industrial separation processes, as it greatly determines the efficacy of distillation in separating components between the liquid and vapor phases. Traditional approaches find it difficult to model K dependence on operational factors such as reflux ratio, molar feed flow rate, and feed composition. This research combines the Aspen Hysys simulation and artificial neural networks (ANNs) to write K as a function of these variables in a benzene-toluene binary system. Aspen Hysys was used to simulate a continuous distillation column that produced K values under different conditions. This data was used to train an ANN (3:4:1 multilayer perceptron) to predict K. The ANN's training performance was 95.3%, and its predictive accuracy was high. This composite method can improve predictive performance in the optimization of distillation column design and operation. It can be effectively used as a tool in industrial practice.
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Copyright (c) 2026 Shahad Z. Al-Najjar, Hayder A. Alhameedi, Salih A. Rushdi, Zainab T. Al-Sharify, Helen Onyeaka, Suzanne Alsamaq (Author)
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